__author__ = 'Stephanie Juneau <stephanie.juneau@noirlab.edu>, Robert Nikutta <robert.nikutta@noirlab.edu>, Astro Data Lab Team <datalab@noirlab.edu>'
__version__ = '20240515' # yyyymmdd
__datasets__ = ['des_dr1']
__keywords__ = ['galaxies', 'object classification', 'plot:color-color', 'stars']
by Stéphanie Juneau, Knut Olsen, Robert Nikutta, and the Astro Data Lab Team
In this notebook, we investigate the optical colors of astronomical sources detected in the Dark Energy Survey (DES), an imaging survey conducted with the Dark Energy Camera (DECam) on the 4-meter Blanco telescope. The first data release, DR1, comprises ~400 millions stars, galaxies and quasars (or QSOs: Quasi-Stellar Objects).
Astro Data Lab products and services used here:
Below, we query the database, compute colors, plot a few color combinations, and take into account the source shapes as defined from the spread of the light profiles and a pre-computed star-galaxy classifier, in order to differentiate between object classes. In this notebook, we focus on star-galaxy separation, and do not yet attempt to classify QSOs.
If you use this notebook for your published science, please acknowledge the following:
An imaging survey includes a zoo of different astronomical objects. There are foreground stars from our own Milky Way galaxy, and background galaxies at various distances, including QSOs with actively accreting supermassive black holes. In this notebook, we aim to distinguish between object classes using combinations of colors and shape parameters.
There are several possible science applications. For example, one could select stars in order to study stellar populations from the Milky Way or search for specific classes of stars (Red Giants, etc.). Alternatively, one can select galaxies to address questions in galaxy evolution or to probe their spatial organization in large-scale structures. Below, we explore possible classifications, compare the results from different bands/filters, and refer to example classifications suggested by the DES team.
Magnitudes are obtained through a set of filters similar to the u,g,r,i,z set used for SDSS. In this work, we use g,r,z from the Dark Energy Camera (DECam)
Colors are defined as a difference between magnitudes in two bands. A "redder" color means that the object is comparatively brighter in the redder (i.e., longer wavelength) band. Conversely, a "bluer" color means that the object is comparatively brighter in the bluer (i.e., shorter wavelength) band.
We will use the following colors:
The object shape (2D light profile) can be defined with a size and ellipticity. In this notebook, we are interested in using extension of the images to distinguish between stars (point sources), galaxies (resolved, if not too distant), and QSOs (which may have both point-like and resolved components).
kron_radius: Kron radius in pixelsspread_model_X: star-galaxy classifier comparing extended model to PSF model. Value is given for each band X = g, r, i, z, yclass_star_X: value ranging from 0.0 (not point-like) to 1.0 (point-like), for each band X = g, r, i, z, yThe value of spread_model is near zero for a point source (star or QSO), positive for an extended source (galaxy), negative for an artifact smaller than the PSF (e.g., bad pixel or cosmic ray). More information about the spread_model can be found in Desai et al (2012), and Soumagnac et al (2015).
# 3rd party
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
# Data Lab
from dl import queryClient as qc, storeClient as sc
from dl.helpers.utils import convert
The photometry is derived from SourceExtractor modeling of sources, and the database includes model photometry, type (shape), as well as aperture photometry in various aperture sizes. In this work, we will use SourceExtractor adaptive aperture magnitudes (MAG_AUTO).
The database is called des_dr1 and includes several tables. We will use the main table. The column names and descriptions can be found from the Data Lab Query Interface or using the Table Access Protocol (TAP) service with, e.g., TOPCAT. In both cases, we are interested in des_dr1.main.
# Write query statement (sql) as a string
# NOTE: triple quotes allow us to break the string on multiple lines
query = """
SELECT mag_auto_g as gmag, mag_auto_r as rmag, mag_auto_z as zmag,
mag_auto_i as imag, mag_auto_y as ymag,
kron_radius, spread_model_g, class_star_g, spread_model_r, class_star_r,
spread_model_z, class_star_z,
snr_g, snr_r, snr_z, ra, dec
FROM des_dr1.main
WHERE (fluxerr_auto_g>0 and fluxerr_auto_r>0 and fluxerr_auto_z>0)
LIMIT 300000"""
# mag_auto_g,r,i,z,y = AB magnitudes in DECam g,r,i,z,y bands
# kron_radius = Kron radius from SExtractor (pixels)
# spread_model_g,r,z = star/galaxy classifier quantifying light profile relative to PSF
# class_star_g,r,z = star/extended source classifier (from 0 to 1)
# snr_g,r,z = computed signal-to-noise ratios (S/N) in g,r,z bands
# ra,dec = celestial coordinates
#
# WHERE: requirement that error>0 (to avoid dividing by zero) in g,r,z bands
# LIMIT: returns 300,000 rows that satisfy the query
print(query)
SELECT mag_auto_g as gmag, mag_auto_r as rmag, mag_auto_z as zmag,
mag_auto_i as imag, mag_auto_y as ymag,
kron_radius, spread_model_g, class_star_g, spread_model_r, class_star_r,
spread_model_z, class_star_z,
snr_g, snr_r, snr_z, ra, dec
FROM des_dr1.main
WHERE (fluxerr_auto_g>0 and fluxerr_auto_r>0 and fluxerr_auto_z>0)
LIMIT 300000
# Execute the query synchronously, if short (e.g., LIMIT<=300,000)
# We then convert the returned result, a CSV-formatted string, to a Pandas data frame,
# making sure that any potential 'Infinity' values are converted to NaN.
res = qc.query(sql=query)
df = convert(res,'pandas',na_values=('Infinity','-Infinity'))
# Print a few rows from the result data frame
print(df[:5])
len(df)
gmag rmag zmag imag ymag kron_radius \ 0 99.000000 23.723530 21.844639 22.748678 21.612795 5.636725 1 24.590650 24.238567 23.377520 23.463213 22.426397 7.349276 2 22.358858 20.941229 19.088203 19.648966 18.954773 3.500000 3 22.800650 21.734190 20.943510 21.251347 21.216537 3.960677 4 24.076948 24.123098 23.687569 23.560404 24.456005 4.853493 spread_model_g class_star_g spread_model_r class_star_r spread_model_z \ 0 1.000000 0.387410 0.007153 0.001145 0.006896 1 0.002813 0.396117 0.015848 0.076924 -0.006221 2 -0.001526 0.487896 -0.000769 0.874804 -0.000278 3 0.007506 0.012388 0.015157 0.022475 0.018248 4 0.006381 0.511646 -0.001337 0.638196 -0.006208 class_star_z snr_g snr_r snr_z ra dec 0 0.025661 -0.700577 5.082931 9.976907 332.817207 -40.772225 1 0.516581 3.695609 4.257700 3.272757 332.821501 -40.775210 2 0.889356 25.641943 77.938965 150.175350 332.816752 -40.768197 3 0.002567 12.703565 28.215712 20.395880 332.821653 -40.771899 4 0.583202 6.781471 5.419320 2.817178 332.821039 -40.768729
300000
# Select range of interest
thres = 5. #threshold value for S/N (here, making it more stringent than query)
keep = (df['snr_g']>thres)&(df['snr_r']>thres)&(df['snr_z']>thres)
# Colors
g_r = df['gmag'][keep] - df['rmag'][keep]
r_z = df['rmag'][keep] - df['zmag'][keep]
r_i = df['rmag'][keep] - df['imag'][keep]
i_z = df['imag'][keep] - df['zmag'][keep]
z_y = df['zmag'][keep] - df['ymag'][keep]
print(f'Count: {len(g_r)}')
Count: 106023
col0 = r_z #r-z color
col1 = g_r #g-r color
# 2D-histogram of objects
fig, ax1 = plt.subplots(1, 1, figsize=(9, 8))
im1 = ax1.hexbin(col0, col1, bins='log', cmap=plt.cm.viridis,
mincnt=1, extent=(-1., 3, -1., 3))
ax1.set_xlabel('r-z',fontsize=20)
ax1.set_ylabel('g-r',fontsize=20)
#color bar
cb = plt.colorbar(im1,label='log(N)')
In the figure above, we can see structure in the number density map such as two curved branches that cross or overlap, along with a more diffuse clump or cloud of objects. We will revisit this color-color plot below after classifying objects into stars and galaxies. This will help to isolate the two branches.
In this section, we make series of plots combining morphological parameters such as spread_model, class_star, and kron_radius with magnitudes (in each of g, r & z bands). The goals are to determine which quantities help to classify objects into stars and galaxies, and to compare the consistency and differences between results obtained with the different bands.
def plot_morph(df,band='g'):
"""
Plots in two columns x two rows
"""
fig = plt.figure(figsize=(8*2,8*2))
ax = fig.add_subplot(2,2,1)
ax.hexbin(df[band+'mag'],df['spread_model_'+band],gridsize=200,bins='log',mincnt=1,extent=(12,26,-0.05,0.05))
ax.set_xlabel(band,fontsize=20)
ax.set_ylabel('spread_model_'+band,fontsize=20)
ax.tick_params(labelsize=10)
ax2 = fig.add_subplot(2,2,2)
ax2.hexbin(df['kron_radius'],df['class_star_'+band],gridsize=200,bins='log',mincnt=1,extent=(0,10,0,1))
ax2.set_xlabel('Kron radius',fontsize=20)
ax2.set_ylabel('class_star_'+band,fontsize=20)
ax2.tick_params(labelsize=10)
ax3 = fig.add_subplot(2,2,3)
ax3.hexbin(df[band+'mag'],df['class_star_'+band],gridsize=200,bins='log',mincnt=1,extent=(12,26,0,1))
ax3.set_xlabel(band,fontsize=20)
ax3.set_ylabel('class_star_'+band,fontsize=20)
ax3.tick_params(labelsize=10)
ax4 = fig.add_subplot(2,2,4)
ax4.hexbin(df['spread_model_'+band],df['class_star_'+band],gridsize=200,bins='log',mincnt=1,extent=(-0.025,0.025,0,1))
ax4.set_xlabel('spread_model_'+band,fontsize=20)
ax4.set_ylabel('class_star_'+band,fontsize=20)
ax4.tick_params(labelsize=10)
First, we plot the results only for the g band, requiring a signal-to-noise > 5 in the g filter.
# invalid mags are set to 99 -> mag_auto_X < 90
# spread_model_X is valid at abs(spread_model)<1
# require S/N>5 in the g band
df_in=df[(df['gmag']<90) & (df['imag']<90) & (df['spread_model_g']>(-1)) & \
(df['rmag']<90) & (df['zmag']<90) & (df['spread_model_g']<1) & \
((df['snr_g'])>5)]
print('Number of objects with S/N>5 & valid spread_model in g band: ',len(df_in))
plot_morph(df_in)
Number of objects with S/N>5 & valid spread_model in g band: 150969
As a reminder, spread_model should be nearly 0 for point-sources, positive for extended sources, and negative for data artifacts smaller than the PSF. The top left panel clearly shows stars along an horizontal sequence around 0 spread. However, we can see that the other group of points (presummably galaxies) join and overlap with the stars at faint magnitudes starting around g=23.
Similarly, class_star values range from 0.0 (not point-like) to 1.0 (point-like). One would thus expect that a confident classification would be nearly 0 for stars, and nearly 1 for resolved galaxies. Indeed, the two panels of the bottom row show two clear branches at low and high values. However, there are also a few ridge-like structures along class_star with values between 0.3-0.7. We suspect that these correspond to lower quality measurements. We will see below that these ridges are less prominent in redder bands (in r, and particularly in z), and that they also vanish when using g band measurements of objects with good detections in all 3 bands.
Second, we plot the results only for the r band, requiring a signal-to-noise > 5 in the r filter.
# invalid mags are set to 99 -> mag_auto_X < 90
# spread_model_X is valid at abs(spread_model)<1
# require S/N>5 in the r band
df_in=df[(df['gmag']<90) & (df['imag']<90) & (df['spread_model_r']>(-1)) & \
(df['rmag']<90) & (df['zmag']<90) & (df['spread_model_r']<1) & \
((df['snr_r'])>5)]
print('Number of objects with S/N>5 & valid spread_model in r band: ',len(df_in))
plot_morph(df_in,band='r')
Number of objects with S/N>5 & valid spread_model in r band: 215545
The plots above are very similar to the equivalent in the g band except that the ridge-like structures along class_star values between 0.3-0.7 are less prominent.
Third, we plot the results only for the z band, requiring a signal-to-noise > 5 in the z filter.
# invalid mags are set to 99 -> mag_auto_X < 90
# spread_model_X is valid at abs(spread_model)<1
# require S/N>5 in the z band
df_in = df[(df['gmag']<90) & (df['imag']<90) & (df['spread_model_z']>(-1)) & \
(df['rmag']<90) & (df['zmag']<90) & (df['spread_model_z']<1) & \
((df['snr_z'])>5)]
print('Number of objects with S/N>5 & valid spread_model in z band: ',len(df_in))
plot_morph(df_in,band='z')
Number of objects with S/N>5 & valid spread_model in z band: 149652
The plots above are very similar to the equivalent in the g & r band except that the ridge-like structures along class_star values between 0.3-0.7 are much less prominent than even in the r band. The overlap between spread_model values for stars and galaxies also seems less severe than for the g and r bands.
Above, the spread_model yields the best separation in the z band, as well as the fewest ridge features in distribution of class_star. On the other hand, the g band shows the most overlap in spread_model values at faint magnitudes, and the most striking ridges in the distribution of class_star. For testing purposes, let's see what happens for cases that have detections in all 3 g,r,z bands, and then when the classification agrees between the bands.
Next, we plot again the "worst" case (g band) but requiring objects to have S/N>5 in all of g,r,z bands.
# invalid mags are set to 99 but requiring S/N>5 will only keep valid mags
# spread_model_X is valid at abs(spread_model)<1
# require S/N>5 in the g, r, z band
df_in=df[(df['spread_model_g']>(-1)) & (df['spread_model_g']<1) & \
(df['snr_g']>5) & (df['snr_r']>5) & (df['snr_z']>5)]
print('Number of objects with S/N>5 in g, r, z bands & valid spread_model in g: ',len(df_in))
plot_morph(df_in,band='g')
Number of objects with S/N>5 in g, r, z bands & valid spread_model in g: 106016
The test above resulted in cleaner plots with fewer ridges on the class_star parameter.
Now, let's test what happens in cases with agreement or disagreement of the value of class_star between bands. We are going to define categories as follows:
class_star < 0.5 in all bandsclass_star < 0.5 in g but not in r or zclass_star > 0.5 in all bandsclass_star > 0.5 in g but not in r or zThe cut at 0.5 is somewhat arbitrary. The primary motivation is to test how the samples behave if they have a consistent classification between all 3 bands, versus a conflicting classification between the g band and at least of the other two bands (r and/or z).
def plot_spread_mag(df_input,i,label=None,**kwargs):
ax = fig.add_subplot(2,2,i+1)
ax.hexbin(df_input['gmag'],df_input['spread_model_g'],gridsize=200,bins='log',mincnt=1,extent=(12,26,-0.05,0.05))
ax.set_xlabel('g',fontsize=20)
ax.set_ylabel('spread_model_g',fontsize=20)
ax.tick_params(labelsize=10)
if label is not None:
ax.text(0.1,0.1,label,transform=ax.transAxes,fontsize=18,color='red',backgroundcolor='white')
# invalid mags are set to 99 -> mag_auto_X < 90
# spread_model_X is valid at abs(spread_model)<1
# require S/N>5 in the g, r, z band
df=df[(df['spread_model_g']>(-1)) & (df['spread_model_g']<1) & \
(df['snr_g']>5) & (df['snr_r']>5) & (df['snr_z']>5)]
df_gal = df[(df['class_star_g']<0.5) & (df['class_star_r']<0.5) & (df['class_star_z']<0.5)]
df_notok = df[(df['class_star_g']<0.5) & ((df['class_star_r']>0.5) | (df['class_star_z']>0.5))]
df_star = df[(df['class_star_g']>0.5) & (df['class_star_r']>0.5) & (df['class_star_z']>0.5)]
df_notok2 = df[(df['class_star_g']>0.5) & ((df['class_star_r']<0.5) | (df['class_star_z']<0.5))]
fig = plt.figure(figsize=(7*2,7*2))
plot_spread_mag(df_gal,0,label='Galaxies (good)')
plot_spread_mag(df_star,1,label='Stars (good)')
plot_spread_mag(df_notok,2,label='Galaxies (bad)')
plot_spread_mag(df_notok2,3,label='Stars (bad)')
# Uncomment the following 2 lines to save a PNG file to your local notebook directory
#outfile = 'class_star_g_TESTS.png'
#fig.savefig(outfile, bbox_inches='tight')
As displayed in the top row, objects with consistent classifications between bands show the cleanest separation. The bottom row displays objects with a conflicting classification. The g-band galaxies with a conflicting r and/or z band classification tend to have spread_model near 0, as expected for point-like sources, but with a large scatter at faint magnitudes or perhaps joining with an actual population of galaxies. The g-band stars with a conflicting r and/or z band classification behave like a mixture of stars with spread_model near 0 and a clear other population contributing at g>23.
We repeat the color-color plot from the Plot results section, but now split into the categories defined above:
class_star < 0.5 in all bandsclass_star < 0.5 in g but not in r or zclass_star > 0.5 in all bandsclass_star > 0.5 in g but not in r or zdef plot_grz(df_input,i,label=None,**kwargs):
col0 = df_input['rmag'] - df_input['zmag'] #r-z color
col1 = df_input['gmag'] - df_input['rmag'] #g-r color
# 2D-histogram of objects
ax = fig.add_subplot(2,2,i+1)
ax.hexbin(col0, col1, bins='log', cmap=plt.cm.viridis, gridsize=140,
mincnt=1, extent=(-1., 3, -1., 3))
ax.set_xlabel('r-z',fontsize=20)
ax.set_ylabel('g-r',fontsize=20)
if label is not None:
ax.text(0.1,0.9,label,transform=ax.transAxes,fontsize=16,color='red',backgroundcolor='white')
fig = plt.figure(figsize=(7*2,7*2))
plot_grz(df_gal,0,label='Galaxies (good)')
plot_grz(df_star,1,label='Stars (good)')
plot_grz(df_notok,2,label='Galaxies (bad)')
plot_grz(df_notok2,3,label='Stars (bad)')
# Uncomment the following 2 lines to save a PNG file to your local notebook directory
#outfile = 'grz_DESDR1.png'
#fig.savefig(outfile, bbox_inches='tight')
The galaxy branch and the stellar locus on the g-r vs. r-z plane are much cleaner. However, it is not clear what happens to the QSO's. It could be that they are mostly in the "cloud" below the branches and most obvious in the panel labeled "Stars (bad)". This means that they would look like a star in g but not in either r or z. At a fixed r-z, QSOs would be bluer than stars in g-r. One could investigate with spectroscopy or templates to confirm.
For reference, this is a figure with LS (DECaLS) photometry for SDSS DR13 spectral classification (comparatively bright objects).
The color-color plots are repeated for bright objects (g < 22 mag) and again split into the four categories defined previously.
# Repeat above with with gmag < 22 as in SDSS/BOSS DR12Q
g22_gal = df[(df['class_star_g']<0.5) & (df['class_star_r']<0.5) & (df['class_star_z']<0.5) & (df['gmag']<22.)]
g22_notok = df[(df['class_star_g']<0.5) & ((df['class_star_r']>0.5) | (df['class_star_z']>0.5)) & (df['gmag']<22.)]
g22_star = df[(df['class_star_g']>0.5) & (df['class_star_r']>0.5) & (df['class_star_z']>0.5) & (df['gmag']<22.)]
g22_notok2 = df[(df['class_star_g']>0.5) & ((df['class_star_r']<0.5) | (df['class_star_z']<0.5)) & (df['gmag']<22.)]
fig = plt.figure(figsize=(7*2,7*2))
plot_grz(g22_gal,0,label='Galaxies (good)')
plot_grz(g22_star,1,label='Stars (good)')
plot_grz(g22_notok,2,label='Galaxies (bad)')
plot_grz(g22_notok2,3,label='Stars (bad)')
# Uncomment the following 2 lines to save a PNG file to your local notebook directory
#outfile = 'grz_DESDR1_gLT22.png'
#fig.savefig(outfile, bbox_inches='tight')
The galaxies and stars with consistent classifications follow tight branches with limited scatter. Interestingly, the g-band galaxies with a conflicting classification (bad) follow the same locus as the good stars. This indicates that the r and z band classification as star is more appropriate when there is a conflict at g<22. Since the g-band stars with a conflicting classification also follow the locus of the good stars, a more general statement would be that objects with a conflicting classification at bright magnitude (g<22) are likely stars.
The color-color plots are repeated for faint objects (g > 23 mag) and again split into the four categories defined previously.
# Repeat above with with gmag > 23 (1 mag fainter than SDSS/BOSS DR12Q)
g23_gal = df[(df['class_star_g']<0.5) & (df['class_star_r']<0.5) & (df['class_star_z']<0.5) & (df['gmag']>23.)]
g23_notok = df[(df['class_star_g']<0.5) & ((df['class_star_r']>0.5) | (df['class_star_z']>0.5)) & (df['gmag']>23.)]
g23_star = df[(df['class_star_g']>0.5) & (df['class_star_r']>0.5) & (df['class_star_z']>0.5) & (df['gmag']>23.)]
g23_notok2 = df[(df['class_star_g']>0.5) & ((df['class_star_r']<0.5) | (df['class_star_z']<0.5)) & (df['gmag']>23.)]
fig = plt.figure(figsize=(7*2,7*2))
plot_grz(g23_gal,0,label='Galaxies (good)')
plot_grz(g23_star,1,label='Stars (good)')
plot_grz(g23_notok,2,label='Galaxies (bad)')
plot_grz(g23_notok2,3,label='Stars (bad)')
# Uncomment the following 2 lines to save a PNG file to your local notebook directory
#outfile = 'grz_DESDR1_gGT23.png'
#fig.savefig(outfile, bbox_inches='tight')
In the plot above, the stellar locus is broad and curved in a way that overlaps with galaxies, and with a very similar shape between good Stars and bad Galaxies. The increased scatter and increased overlap make using these colors for object identification more difficult compared to the situation for bright objects. Further investigation might help to identify the actual nature of the objects shown above. One possibility would be to fold in infrared photometry from WISE.
This section includes notes on a possible star-galaxy classification from the DES team. Please see the DES DR1 paper for the latest definition. It could be interesting to make a direct comparison with the categories above.
des_dr1.main is the table that would be used at Data Lab.
Stars:
WHERE
( (CASE WHEN spread_model_i + 3. * spreaderr_model_i > 0.005 THEN 1 ELSE 0 END) +
(CASE WHEN spread_model_i + 1. * spreaderr_model_i > 0.003 THEN 1 ELSE 0 END) +
(CASE WHEN spread_model_i - 1. * spreaderr_model_i > 0.003 THEN 1 ELSE 0 END) ) <= 1
AND spread_model_i BETWEEN -0.05 AND 0.05
AND imaflags_iso_i = 0
AND flags_i < 4
Galaxies:
WHERE
( (CASE WHEN spread_model_i + 3. * spreaderr_model_i > 0.005 THEN 1 ELSE 0 END) +
(CASE WHEN spread_model_i + 1. * spreaderr_model_i > 0.003 THEN 1 ELSE 0 END) +
(CASE WHEN spread_model_i - 1. * spreaderr_model_i > 0.003 THEN 1 ELSE 0 END) ) = 3
AND spread_model_i BETWEEN -0.05 AND 0.05
AND imaflags_iso_i = 0
AND flags_i < 4
AND mag_auto_i < 23
Please note that if using other bands such as g and r, one might need to also require:
imaflags_iso_g = 0
imaflags_iso_r = 0
flags_g < 4
flags_r < 4
The exact cuts could be adapted slightly depending on the desired trade-off between purity versus completeness in the sample selection.
class_star comparison¶We compare, for the 4 categories defined above, the class_star values between g and r bands, and between g and z bands. Cases with consistent classifications in all 3 bands should only have points in the corresponding quadrant (<0.5 for galaxies or >0.5 for stars). Given that the inconsistent "bad" classifications means a conflict with the r classification and/or the z classification, the distributions will spread over two quadrants in these cases.
def plot_class_star_gr(df_input,i,label=None,**kwargs):
ax = fig.add_subplot(2,4,i+1)
ax.hexbin(df_input['class_star_r'],df_input['class_star_g'],gridsize=200,bins='log',mincnt=1,extent=(0,1,0,1.2))
ax.set_xlabel('class_star_r',fontsize=10)
ax.set_ylabel('class_star_g',fontsize=10)
ax.tick_params(labelsize=10)
if label is not None:
ax.text(0.1,0.9,label,transform=ax.transAxes,fontsize=16,color='red',backgroundcolor='white')
def plot_class_star_gz(df_input,i,label=None,**kwargs):
ax = fig.add_subplot(2,4,i+1)
ax.hexbin(df_input['class_star_z'],df_input['class_star_g'],gridsize=200,bins='log',mincnt=1,extent=(0,1,0,1.2))
ax.set_xlabel('class_star_z',fontsize=10)
ax.set_ylabel('class_star_g',fontsize=10)
ax.tick_params(labelsize=10)
if label is not None:
ax.text(0.1,0.9,label,transform=ax.transAxes,fontsize=16,color='red',backgroundcolor='white')
fig = plt.figure(figsize=(4*4,4.5*2))
plot_class_star_gr(df_gal,0,label='Galaxies (good)')
plot_class_star_gr(df_star,1,label='Stars (good)')
plot_class_star_gr(df_notok,2,label='Galaxies (bad)')
plot_class_star_gr(df_notok2,3,label='Stars (bad)')
plot_class_star_gz(df_gal,4,label='Galaxies (good)')
plot_class_star_gz(df_star,5,label='Stars (good)')
plot_class_star_gz(df_notok,6,label='Galaxies (bad)')
plot_class_star_gz(df_notok2,7,label='Stars (bad)')
# Uncomment the following 2 lines to save a PNG file to your local notebook directory
#outfile = 'class_star_g_COMPARE.png'
#fig.savefig(outfile, bbox_inches='tight')
